Enhancing Neural Spoken Language Recognition: An Exploration with Multilingual Datasets

TL;DR

This paper presents a novel multilingual neural spoken language recognition system that employs optimized Time Delay Neural Networks with a specialized pooling layer, achieving 97% accuracy across ten diverse languages.

Contribution

It introduces an improved TDN architecture with a funnel shape and extensive hyperparameter tuning, advancing multilingual speech recognition capabilities.

Findings

Achieved 97% language recognition accuracy.

Enhanced TDN architecture with additional layers and funnel structure.

Effective use of augmented data for training.

Abstract

In this research, we advanced a spoken language recognition system, moving beyond traditional feature vector-based models. Our improvements focused on effectively capturing language characteristics over extended periods using a specialized pooling layer. We utilized a broad dataset range from Common-Voice, targeting ten languages across Indo-European, Semitic, and East Asian families. The major innovation involved optimizing the architecture of Time Delay Neural Networks. We introduced additional layers and restructured these networks into a funnel shape, enhancing their ability to process complex linguistic patterns. A rigorous grid search determined the optimal settings for these networks, significantly boosting their efficiency in language pattern recognition from audio samples. The model underwent extensive training, including a phase with augmented data, to refine its capabilities. The culmination of these efforts is a highly accurate system, achieving a 97\% accuracy rate in language recognition. This advancement represents a notable contribution to artificial intelligence, specifically in improving the accuracy and efficiency of language processing systems, a critical aspect in the engineering of advanced speech recognition technologies.